from keras.datasets import mnist from keras.models import Sequential from keras.layers import Dense, Dropout, Activation, Flatten from keras.layers import Convolution2D, MaxPooling2D from keras.utils import np_utils from keras import backend as K class LossHistory(keras.callbacks.Callback): def on_train_begin(self, logs={}): self.losses = {'batch':[], 'epoch':[]} self.accuracy = {'batch':[], 'epoch':[]} self.val_loss = {'batch':[], 'epoch':[]} self.val_acc = {'batch':[], 'epoch':[]} def on_batch_end(self, batch, logs={}): self.losses['batch'].append(logs.get('loss')) self.accuracy['batch'].append(logs.get('acc')) self.val_loss['batch'].append(logs.get('val_loss')) self.val_acc['batch'].append(logs.get('val_acc')) def on_epoch_end(self, batch, logs={}): self.losses['epoch'].append(logs.get('loss')) self.accuracy['epoch'].append(logs.get('acc')) self.val_loss['epoch'].append(logs.get('val_loss')) self.val_acc['epoch'].append(logs.get('val_acc')) def loss_plot(self, loss_type): iters = range(len(self.losses[loss_type])) plt.figure() # acc plt.plot(iters, self.accuracy[loss_type], 'r', label='train acc') # loss plt.plot(iters, self.losses[loss_type], 'g', label='train loss') if loss_type == 'epoch': # val_acc plt.plot(iters, self.val_acc[loss_type], 'b', label='val acc') # val_loss plt.plot(iters, self.val_loss[loss_type], 'k', label='val loss') plt.grid(True) plt.xlabel(loss_type) plt.ylabel('acc-loss') plt.legend(loc="upper right") plt.show() history = LossHistory() batch_size = 128 nb_classes = 10 nb_epoch = 20 img_rows, img_cols = 28, 28 nb_filters = 32 pool_size = (2,2) kernel_size = (3,3) (X_train, y_train), (X_test, y_test) = mnist.load_data() X_train = X_train.reshape(X_train.shape[0], img_rows, img_cols, 1) X_test = X_test.reshape(X_test.shape[0], img_rows, img_cols, 1) input_shape = (img_rows, img_cols, 1) X_train = X_train.astype('float32') X_test = X_test.astype('float32') X_train /= 255 X_test /= 255 print('X_train shape:', X_train.shape) print(X_train.shape[0], 'train samples') print(X_test.shape[0], 'test samples') Y_train = np_utils.to_categorical(y_train, nb_classes) Y_test = np_utils.to_categorical(y_test, nb_classes) model3 = Sequential() model3.add(Convolution2D(nb_filters, kernel_size[0] ,kernel_size[1], border_mode='valid', input_shape=input_shape)) model3.add(Activation('relu')) model3.add(Convolution2D(nb_filters, kernel_size[0], kernel_size[1])) model3.add(Activation('relu')) model3.add(MaxPooling2D(pool_size=pool_size)) model3.add(Dropout(0.25)) model3.add(Flatten()) model3.add(Dense(128)) model3.add(Activation('relu')) model3.add(Dropout(0.5)) model3.add(Dense(nb_classes)) model3.add(Activation('softmax')) model3.summary() model3.compile(loss='categorical_crossentropy', optimizer='adadelta', metrics=['accuracy']) model3.fit(X_train, Y_train, batch_size=batch_size, epochs=nb_epoch, verbose=1, validation_data=(X_test, Y_test),callbacks=[history]) score = model3.evaluate(X_test, Y_test, verbose=0) print('Test score:', score[0]) print('Test accuracy:', score[1]) #acc-loss history.loss_plot('epoch')
補充:使用keras全連接網(wǎng)絡訓練mnist手寫數(shù)字識別并輸出可視化訓練過程以及預測結果
mnist 數(shù)字識別問題的可以直接使用全連接實現(xiàn)但是效果并不像CNN卷積神經(jīng)網(wǎng)絡好。Keras是目前最為廣泛的深度學習工具之一,底層可以支持Tensorflow、MXNet、CNTK、Theano
TensorFlow版本:1.13.1
Keras版本:2.1.6
Numpy版本:1.18.0
matplotlib版本:2.2.2
from keras.layers import Dense,Flatten,Dropout from keras.datasets import mnist from keras import Sequential import matplotlib.pyplot as plt import numpy as np
Dense輸入層作為全連接,F(xiàn)latten用于全連接扁平化操作(也就是將二維打成一維),Dropout避免過擬合。使用datasets中的mnist的數(shù)據(jù)集,Sequential用于構建模型,plt為可視化,np用于處理數(shù)據(jù)。
# 訓練集 訓練集標簽 測試集 測試集標簽 (train_image,train_label),(test_image,test_label) = mnist.load_data() print('shape:',train_image.shape) #查看訓練集的shape plt.imshow(train_image[0]) #查看第一張圖片 print('label:',train_label[0]) #查看第一張圖片對應的標簽 plt.show()
輸出shape以及標簽label結果:
查看mnist數(shù)據(jù)集中第一張圖片:
train_image = train_image.astype('float32') test_image = test_image.astype('float32') train_image /= 255.0 test_image /= 255.0
將數(shù)據(jù)歸一化,以便于訓練的時候更快的收斂。
#初始化模型(模型的優(yōu)化 ---> 增大網(wǎng)絡容量,直到過擬合) model = Sequential() model.add(Flatten(input_shape=(28,28))) #將二維扁平化為一維(60000,28,28)---> (60000,28*28)輸入28*28個神經(jīng)元 model.add(Dropout(0.1)) model.add(Dense(1024,activation='relu')) #全連接層 輸出64個神經(jīng)元 ,kernel_regularizer=l2(0.0003) model.add(Dropout(0.1)) model.add(Dense(512,activation='relu')) #全連接層 model.add(Dropout(0.1)) model.add(Dense(256,activation='relu')) #全連接層 model.add(Dropout(0.1)) model.add(Dense(10,activation='softmax')) #輸出層,10個類別,用softmax分類
每層使用一次Dropout防止過擬合,激活函數(shù)使用relu,最后一層Dense神經(jīng)元設置為10,使用softmax作為激活函數(shù),因為只有0-9個數(shù)字。如果是二分類問題就使用sigmod函數(shù)來處理。
#編譯模型 model.compile( optimizer='adam', #優(yōu)化器使用默認adam loss='sparse_categorical_crossentropy', #損失函數(shù)使用sparse_categorical_crossentropy metrics=['acc'] #評價指標 )
sparse_categorical_crossentropy與categorical_crossentropy的區(qū)別:
sparse_categorical_crossentropy要求target為非One-hot編碼,函數(shù)內部進行One-hot編碼實現(xiàn)。
categorical_crossentropy要求target為One-hot編碼。
One-hot格式如: [0,0,0,0,0,1,0,0,0,0] = 5
#訓練模型 history = model.fit( x=train_image, #訓練的圖片 y=train_label, #訓練的標簽 epochs=10, #迭代10次 batch_size=512, #劃分批次 validation_data=(test_image,test_label) #驗證集 )
迭代10次后的結果:
#繪制loss acc圖 plt.figure() plt.plot(history.history['acc'],label='training acc') plt.plot(history.history['val_acc'],label='val acc') plt.title('model acc') plt.ylabel('acc') plt.xlabel('epoch') plt.legend(loc='lower right') plt.figure() plt.plot(history.history['loss'],label='training loss') plt.plot(history.history['val_loss'],label='val loss') plt.title('model loss') plt.ylabel('loss') plt.xlabel('epoch') plt.legend(loc='upper right') plt.show()
繪制出的loss變化圖:
繪制出的acc變化圖:
print("前十個圖片對應的標簽: ",test_label[:10]) #前十個圖片對應的標簽 print("取前十張圖片測試集預測:",np.argmax(model.predict(test_image[:10]),axis=1)) #取前十張圖片測試集預測
打印的結果:
可看到在第9個數(shù)字預測錯了,標簽為5的,預測成了6,為了避免這種問題可以適當?shù)募由罹W(wǎng)絡結構,或使用CNN模型。
model.save('./mnist_model.h5')
from keras.layers import Dense,Flatten,Dropout from keras.datasets import mnist from keras import Sequential import matplotlib.pyplot as plt import numpy as np # 訓練集 訓練集標簽 測試集 測試集標簽 (train_image,train_label),(test_image,test_label) = mnist.load_data() # print('shape:',train_image.shape) #查看訓練集的shape # plt.imshow(train_image[0]) #查看第一張圖片 # print('label:',train_label[0]) #查看第一張圖片對應的標簽 # plt.show() #歸一化(收斂) train_image = train_image.astype('float32') test_image = test_image.astype('float32') train_image /= 255.0 test_image /= 255.0 #初始化模型(模型的優(yōu)化 ---> 增大網(wǎng)絡容量,直到過擬合) model = Sequential() model.add(Flatten(input_shape=(28,28))) #將二維扁平化為一維(60000,28,28)---> (60000,28*28)輸入28*28個神經(jīng)元 model.add(Dropout(0.1)) model.add(Dense(1024,activation='relu')) #全連接層 輸出64個神經(jīng)元 ,kernel_regularizer=l2(0.0003) model.add(Dropout(0.1)) model.add(Dense(512,activation='relu')) #全連接層 model.add(Dropout(0.1)) model.add(Dense(256,activation='relu')) #全連接層 model.add(Dropout(0.1)) model.add(Dense(10,activation='softmax')) #輸出層,10個類別,用softmax分類 #編譯模型 model.compile( optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['acc'] ) #訓練模型 history = model.fit( x=train_image, #訓練的圖片 y=train_label, #訓練的標簽 epochs=10, #迭代10次 batch_size=512, #劃分批次 validation_data=(test_image,test_label) #驗證集 ) #繪制loss acc 圖 plt.figure() plt.plot(history.history['acc'],label='training acc') plt.plot(history.history['val_acc'],label='val acc') plt.title('model acc') plt.ylabel('acc') plt.xlabel('epoch') plt.legend(loc='lower right') plt.figure() plt.plot(history.history['loss'],label='training loss') plt.plot(history.history['val_loss'],label='val loss') plt.title('model loss') plt.ylabel('loss') plt.xlabel('epoch') plt.legend(loc='upper right') plt.show() print("前十個圖片對應的標簽: ",test_label[:10]) #前十個圖片對應的標簽 print("取前十張圖片測試集預測:",np.argmax(model.predict(test_image[:10]),axis=1)) #取前十張圖片測試集預測 #優(yōu)化前(一個全連接層(隱藏層)) #- 1s 12us/step - loss: 1.8765 - acc: 0.8825 # [7 2 1 0 4 1 4 3 5 4] # [7 2 1 0 4 1 4 9 5 9] #優(yōu)化后(三個全連接層(隱藏層)) #- 1s 14us/step - loss: 0.0320 - acc: 0.9926 - val_loss: 0.2530 - val_acc: 0.9655 # [7 2 1 0 4 1 4 9 5 9] # [7 2 1 0 4 1 4 9 5 9] model.save('./model_nameALL.h5')
使用全連接層訓練得到的最后結果train_loss: 0.0242 - train_acc: 0.9918 - val_loss: 0.0560 - val_acc: 0.9826,由loss acc可視化圖可以看出訓練有著明顯的效果。
以上為個人經(jīng)驗,希望能給大家一個參考,也希望大家多多支持腳本之家。
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